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| # -*- coding: utf-8 -*- | |
| import IPython.display as display | |
| import tensorflow as tf | |
| import matplotlib.pyplot as plt | |
| import matplotlib as mpl | |
| import tensorflow_hub as hub | |
| mpl.rcParams['figure.figsize'] = (12,12) | |
| mpl.rcParams['axes.grid'] = False | |
| import numpy as np | |
| import PIL.Image | |
| import time | |
| import functools | |
| def imshow(image, title=None): | |
| if len(image.shape) > 3: | |
| image = tf.squeeze(image, axis=0) | |
| plt.imshow(image) | |
| if title: | |
| plt.title(title) | |
| def load_img(path_to_img): | |
| max_dim = 512 | |
| img = tf.io.read_file(path_to_img) | |
| print(img) | |
| img = tf.image.decode_image(img, channels=3) | |
| img = tf.image.convert_image_dtype(img, tf.float32) | |
| shape = tf.cast(tf.shape(img)[:-1], tf.float32) | |
| long_dim = max(shape) | |
| scale = max_dim / long_dim | |
| new_shape = tf.cast(shape * scale, tf.int32) | |
| img = tf.image.resize(img, new_shape) | |
| img = img[tf.newaxis, :] | |
| return img | |
| def tensor_to_image(tensor): | |
| tensor = tensor*255 | |
| tensor = np.array(tensor, dtype=np.uint8) | |
| if np.ndim(tensor)>3: | |
| assert tensor.shape[0] == 1 | |
| tensor = tensor[0] | |
| return PIL.Image.fromarray(tensor) | |
| content_path = tf.keras.utils.get_file('YellowLabradorLooking_new.jpg', 'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg') | |
| style_path = tf.keras.utils.get_file('kandinsky5.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg') | |
| content_image = load_img(content_path) | |
| style_image = load_img(style_path) | |
| plt.subplot(1, 2, 1) | |
| imshow(content_image, 'Content Image') | |
| plt.subplot(1, 2, 2) | |
| imshow(style_image, 'Style Image') | |
| style_path | |
| hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2') | |
| #hub_model = hub.load('https://hub.tensorflow.google.cn/sayakpaul/lite-model/arbitrary-image-stylization-inceptionv3-dynamic-shapes/dr/predict/1') | |
| stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0] | |
| tensor_to_image(stylized_image) | |
| import numpy as np | |
| import gradio as gr | |
| def sepia(input_path,input_path2): | |
| input_img = load_img(input_path) | |
| input_img2 = load_img(input_path2) | |
| hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2') | |
| stylized_image = hub_model(tf.constant(input_img), tf.constant(input_img2))[0] | |
| pre_img = tensor_to_image(stylized_image) | |
| return pre_img | |
| demo_path="1.jpg" | |
| demo_path2="2.jpg" | |
| demo = gr.Interface( | |
| sepia,examples=[[demo_path,demo_path2],], | |
| title="上传两个图片，将图片转成另一图片风格", | |
| inputs=[gr.inputs.Image(label="待转换图片",type="filepath"),gr.inputs.Image(label="风格图片",type="filepath")], | |
| outputs=gr.outputs.Image(type="pil", label="输出"),theme="dark-seafoam" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |